Homomorphic Encryption for EDSS Classification: Safeguarding Patient Privacy in MRI-Based Assessment of Multiple Sclerosis

Hospitals and healthcare organizations collect vast amounts of patient data, such as MRI scans, which hold significant potential for advancing automated clinical support systems. However, privacy concerns and the lack of robust data anonymization and protection mechanisms often hinder data sharing and collaborative research. To this end, privacy-preserving and data sanitization techniques have emerged as a promising direction. Among them, Homomorphic Encryption (HE) allows computations to be performed directly on encrypted data without requiring decryption, thereby safeguarding sensitive information throughout the analytical pipeline. In this paper, we investigate the feasibility of leveraging homomorphic encryption to enable Expanded Disability Status Scale (EDSS) classification in Multiple Sclerosis (MS). Thus, we design a dedicated neural network, namely Hybrid AHE-CNN, tailored for processing images together with homomorphically encrypted sensitive data, allowing for secure and privacy-preserving inference without exposing raw patient data. Experimental results demonstrate that our proposed method achieves classification performance comparable to that of models trained and evaluated on plaintext data, highlighting the practical applicability of HE in real-world healthcare settings.